Monday, January 21, 2013

Scikit-Learn 0.13 released! We want your feedback.

After a little delay, the team finished work on the 0.13 release of scikit-learn.
There is also a user survey that we launched in parallel with the release, to get some feedback from our users.

There is a list of changes and new features on the website.
You can upgrade using easy-install or pip using:

pip install -U scikit-learn
or
easy_install -u scikit-learn

There were more than 60 people contributing to this release, with 24 people having 10 commits or more.

Again many improvements are behind the scenes or only slightly notable. We improved test coverage a lot and we have much more consistent parameter names now. There is now also a user guide entry for the classification metrics, and their naming was improved.

This was one of the many improvements Arnaud Joly, who joined the project very recently but nevertheless wound up being the one with the second most commits in this release!

Now let me get to some of the more visible highlights of this release from my perspective:

- Thanks to Lars and Olivier, the Hashing Trick finally made it into scikit-learn.
This allows for very fast vectorization of large text corpora and stateless transformers for the same.

- Sample weights were added to the tree module thanks to Noel and Gilles. This enabled the implementation of a smarter resampling for random forests, which leads to a speed-up of random forests of up to a factor of two! Also, this is the basis of including AdaBoost with Trees in the next release.